Department of Neurobiology, Integrative Center for Learning and Memory, University of California-Los Angeles, Los Angeles, California 90095.
Department of Psychology, University of California-Los Angeles, Los Angeles, California 90095.
J Neurosci. 2023 Jan 4;43(1):82-92. doi: 10.1523/JNEUROSCI.1104-22.2022. Epub 2022 Nov 18.
Cortical computations emerge from the dynamics of neurons embedded in complex cortical circuits. Within these circuits, neuronal ensembles, which represent subnetworks with shared functional connectivity, emerge in an experience-dependent manner. Here we induced ensembles in cortical circuits from mice of either sex by differentially activating subpopulations through chronic optogenetic stimulation. We observed a decrease in voltage correlation, and importantly a synaptic decoupling between the stimulated and nonstimulated populations. We also observed a decrease in firing rate during Up-states in the stimulated population. These ensemble-specific changes were accompanied by decreases in intrinsic excitability in the stimulated population, and a decrease in connectivity between stimulated and nonstimulated pyramidal neurons. By incorporating the empirically observed changes in intrinsic excitability and connectivity into a spiking neural network model, we were able to demonstrate that changes in both intrinsic excitability and connectivity accounted for the decreased firing rate, but only changes in connectivity accounted for the observed decorrelation. Our findings help ascertain the mechanisms underlying the ability of chronic patterned stimulation to create ensembles within cortical circuits and, importantly, show that while Up-states are a global network-wide phenomenon, functionally distinct ensembles can preserve their identity during Up-states through differential firing rates and correlations. The connectivity and activity patterns of local cortical circuits are shaped by experience. This experience-dependent reorganization of cortical circuits is driven by complex interactions between different local learning rules, external input, and reciprocal feedback between many distinct brain areas. Here we used an approach to demonstrate how simple forms of chronic external stimulation can shape local cortical circuits in terms of their correlated activity and functional connectivity. The absence of feedback between different brain areas and full control of external input allowed for a tractable system to study the underlying mechanisms and development of a computational model. Results show that differential stimulation of subpopulations of neurons significantly reshapes cortical circuits and forms subnetworks referred to as neuronal ensembles.
皮层计算源自于嵌入在复杂皮层回路中的神经元的动力学。在这些回路中，神经元集合以经验依赖的方式出现，代表具有共享功能连接的子网。在这里，我们通过慢性光遗传学刺激来差异激活亚群，从而在雄性和雌性小鼠的皮层回路中诱导集合。我们观察到电压相关性降低，并且重要的是，刺激和非刺激群体之间的突触解耦。我们还观察到在刺激群体的 Up 状态期间，放电率降低。这些集合特异性变化伴随着刺激群体中内在兴奋性的降低，以及刺激和非刺激锥体神经元之间的连接减少。通过将内在兴奋性和连接性的经验观察到的变化纳入尖峰神经网络模型，我们能够证明内在兴奋性和连接性的变化都可以解释放电率的降低，但只有连接性的变化可以解释观察到的去相关。我们的发现有助于确定慢性模式刺激在皮层回路中创建集合的机制，并且重要的是，表明虽然 Up 状态是一种全局网络范围的现象，但通过不同的放电率和相关性，功能上不同的集合可以在 Up 状态下保持其身份。局部皮质电路的连接和活动模式是由经验塑造的。这种依赖于经验的皮质回路重组是由不同局部学习规则、外部输入和许多不同脑区之间的相互反馈之间的复杂相互作用驱动的。在这里，我们使用一种方法来证明简单形式的慢性外部刺激如何根据其相关活动和功能连接来塑造局部皮质电路。不同脑区之间缺乏反馈和外部输入的完全控制，使得可用于研究潜在机制和开发计算模型的可行系统。结果表明，神经元亚群的差异刺激显著重塑了皮层回路，并形成了称为神经元集合的子网。
